It is challenging to detect patterns in a large data set that represents events that occurred between sources and targets over time. In particular, it can be difficult to identify a small subset of data that represents an abnormal pattern from a large data set over a temporal period. Identifying such a pattern is akin to finding a “needle in a haystack.” It would be helpful to be able to visually organize the large data set so that the pattern is more readily identifiable. Such techniques can be helpful in identifying patterns in various practical application areas, including detecting healthcare fraud and insider trading.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.